The difference between Reporting and Analytics

A necessary disambiguation between two valuable — and very different — forms of business intelligence.

Annie Musgrove

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In a lot of business content you read these days, “reporting” and “analytics” are two words used interchangeably to describe the general application and use of data — to track the ongoing health of the company and to inform decision making.

To me, “analytics” is a fancier word, with a sophisticated edge that implies applying more intellect and reaping more insight.

Perhaps that’s why many people in the industry have started using it when actually they’re referring to, or are describing, plain old (but still fundamental and important!) reporting.

The fact is that each term corresponds to two very different functions, which provide different value to your business.

And to confuse the terms is to lose the crucial distinction between measuring your performance and investigating your performance.

Definitions

Reporting is “the process of organizing data into informational summaries in order to monitor how different areas of a business are performing.”

Measuring core metrics and presenting them — whether in an email, a slidedeck, or online dashboard — falls under this category.

Analytics is “the process of exploring data and reports in order to extract meaningful insights, which can be used to better understand and improve business performance.”

Differences in value

What’s so special about analytics?

Analytics explains the “why?” and the “so what?”

How? Because it’s dynamic — on more than just a scale of time or interval. You should be able to contort the data to whatever you need. If what you see is a set of standard metrics, then it isn’t truly an analytic assessment (or an analytics product).

From this dynamic investigation into the data, you can derive real-life recommendations about your business. What should you change about your practices to improve your core metrics?

In this way, analytics is also special because it’s extremely actionable. In fact, it must be acted upon. The value of analytics is only truly delivered upon your own follow up, only if you come up with those recommended next steps and execute them. More on that below.

How are each presented visually?

Let’s use Monthly Recurring Revenue as an example.

Reporting: Here is how MRR is typically reported. This chart shows the MRR for the last year, marked monthly.

If you want to analyze your MRR, you can play with the parameters and drill into different nooks and crannies of the data.

Analytics: Here is MRR spliced by marketing channel, over the same twelve months as above.

Or you can get even more pointed.

Here is MRR split by four sales representatives, limited to the last 90 days and marked weekly. This would come in handy not only for routine management but also for quarterly performance evaluations.

Specific forms of analytics

Analytics can be ad-hoc sessions or routine deep-dives.

Cohort analysis

Cohort analysis is a practice of analyzing data by groups of customers, where all customers in the group share a certain attribute. In SaaS this attribute is almost always the time period when users signed up for the service — so there is a January 2016 cohort containing customers who signed up that month, February 2016 and so on. From there you can see how various cohorts behave over time.

If the February 2016 cohort’s behavior is remarkably different from the average pattern — let’s say the ongoing retention is much better — then you can dig into the data further to understand the enhanced performance.

Were there any different customer acquisition strategies for the month of February?

Perhaps a new marketing channel attracted more qualified leads?

Did sales try out a new pitch that set more reasonable customer expectations?

MRR Churn in the last 12 months. This is the most common visualization of a Cohort Analysis in SaaS, but there are other ways, too.

Cohort analysis is a common and routine practice, which often brings it into the fold of reporting. While it’s helpful in any event with any metric, it’s an especially vital part of any analysis of churn because it reveals when, during the customer lifecycle, a customer is most likely to cancel. Customer success teams can use these insights to work proactively and preempt churn.

Segmentation

Segmentation is the practice of splicing your data, distinguishing groups based on different attributes. For example, you can segment your customer data by industry vertical, to see how customer behavior differs based on what kind of business they are. Or you can segment your revenue data by region, to identify what areas of the world are most lucrative and hold the most opportunity for your business.

Segmentation is, one the one hand, a very basic concept — and then very complex on the other. That’s because you can do so much with it. For an example of how dynamic and flexible segmentation can be, check out one of our feature updates, where we try to explain all the ways you can splice and dice your data.

The follow-up to analytics

Investing the time, tools, and personnel in analytics is only worth it if you, well, do something about it. The real meat of analytics lies in using the findings to inform practical and tactical elements of your business. Improving best practices so that metrics improve — this is the value add.

Quick rundown of a follow-up process:

Assemble the results of the analysis as well as likely explanations for those results.

Communicate findings to key players. This can include employees at any level. When it comes to numbers and performance, transparency is the best policy. It fosters understanding of the metrics and of one’s impact, buy-in and enthusiasm for new goals and tactics, and ultimately the foundation for a data-driven culture.

Conclusion

The bottom line is that both reporting and analytics are necessary, and both are very valuable.

But they are not the same thing.

Buzzwords often pick up in the tech world, and all the sudden everyone is using word ‘X’ to describe thing ‘Y.’ Words shift meaning based on how they’re branded. I think that’s what’s at risk with “reporting” and “analytics.” Great analytic efforts are misrepresented as reporting, and standard KPI displays are hyped as analytics.

In anything, losing vigilance with language costs us clarity. In business, if we confuse terms like this then we don’t clearly see the function we’re actually performing (or paying for). As a result of that we might also miss the opportunities the function reveals.

If this seems like a dramatic take on the issue, that’s just because at ChartMogul our whole mission is around helping our customers build a better subscription business.

We provide a reporting and analytics platform, and we want our product to not only inform but also empower our users. Everyone knows the value of KPI reporting, but it can be difficult to demonstrate the power that’s possible in analytics — even harder if someone has a curtailed or diluted idea of what “analytics” is.

So I hope this disambiguation has been helpful! Please now go forth, seize your data, and engage in both reporting and analytics to strengthen your team and grow your business.